What does data mining involve?

In recent years, databases have become an essential tool that every company needs, giving us the chance to manage information far more precisely, improve data quality, and more.

But, as we all know, today we have a huge amount of data that must be turned into useful information so that it gives us real insight and lets us make much sounder, better-informed decisions.

The key to using data mining is that it lets us get the most out of information by discovering patterns or algorithms that allow us to focus on the information that is really useful to us.

What does data mining mean?

Data mining is the whole set of technologies and techniques that allow us to automatically explore large databases to find recurring patterns that explain the behaviour of that data.

Although the concept of data mining may seem a very recent innovation, it actually first appeared in the sixties and became established in the eighties.

Advantages and disadvantages of data mining

The analyses we carry out through data mining bring us many advantages in terms of optimising and managing our time, but we may also encounter certain drawbacks when applying these techniques, depending on the types of data we want to collect or investigate.

Advantages:

·Discovery of information we did not expect

·Ability to analyse and investigate large databases

·Analyses that are easy to interpret

·The chance to find, attract and retain customers

·Noticeable improvement in the service we offer customers

·Offering customers proposals suited to their needs

·Detailed statistical measurement

·Cost savings

·Opening up new business opportunities

Disadvantages:

·It requires a lot of work

·High initial investment

·Privacy and security issues

·Need for specialised staff

Free tools for data mining

Once our databases are up to date, we face a big problem: this process is very complex and rather slow. That is why we have a series of tools and techniques that help us speed up the process of exploring and analysing these databases.

Integrate.io

A platform that makes it easier to prepare and receive data analyses, on which companies can build their own data pipeline. It also includes a customer support service and many features that can be customised to your needs.

https://www.youtube.com/watch?v=z07G72SnPZ0 

Weka

Weka is the tool of choice if we want to obtain data in a much more visual way, as the platform has various visual tools that help strengthen the analysis of the data collected. To get the most out of the tool, our data needs to be in a flat format. The platform also offers regression, visualisation and processing options.

https://www.youtube.com/watch?v=tSq99yYK_DY 

Teradata

This is one of the most suitable options for all companies, as it is a platform that collects specific data on sales, consumer preferences and product placement. It is mainly used as a tool to optimise business processes and to get to know the target audience in more detail.

https://www.youtube.com/watch?v=8ThOOl0X1Yc 

Revolution

Of all the tools, this is the easiest to use and, like integrate.io, to interpret, as it extracts statistical information visually through charts, tables, etc. It focuses more on optimising data analysis than on speeding up the mining. 

Stages of data mining

Data mining, like any technological process, follows these phases or stages:

1.Business understanding

First of all, we must define the objectives we want to achieve while exchanging and cross-referencing all the information we have about the tasks needed to meet the goals we have set.

2.Data understanding

Once we are clear about the objectives and the information needed, we will check the quality and reliability of this data by asking ourselves questions in order to understand exactly what it is for.

3.Data preparation

Next, we will encode the variables we consider essential according to our needs. This is the most time-consuming and labour-intensive phase of the whole data mining process.

4.Modelling

In this phase we will carry out all the procedures to obtain the analyses and the answers to the questions we had set ourselves using various parameters or models.

5.Evaluation

At this point, we will compare the models and parameters we have used against criteria of quality, effectiveness, accuracy and speed.

6.Deployment of the results

Finally, we must process, summarise and present the results we have obtained in a much more visual and understandable way to make them easier to read.

Big data and data mining

Both data mining and big data are related to data, but in different ways. Big data manages large volumes of data whereas data mining focuses more on extracting and analysing the data in these databases.

Even so, the two concepts can also be combined to strengthen each other, since to extract the value of big data and discover patterns in databases we need to carry out data mining.

In conclusion, the two complement each other perfectly if we regard big data as the tool and data mining as the techniques for making the most of the database management that big data makes possible.